MyoSuite baselines

We include several pretrained baselines for MyoSuite and MyoChallenge2023 environments. This includes straight walking for myoLegWalk-v0, standing for myoChallengeChaseTagP1-v0 and cube lifting for myoChallengeRelocateP1-v0.

To try the baselines, you need to first install myosuite==2.0.1. You can play with the pre-trained baselines by using the code in this section. To train agents yourself, go to the Configuration files section.

Pre-trained baselines for myosuite.

environment id	description
myoLegWalk-v0	Train a straight walking myoLeg agent.
myoChallengeChaseTagP1-v0	Used to create the ChaseTag baseline, but rewards are not provided.
myoChallengeRelocateP1-v0	Used to create the Relocate baseline, but rewards are not provided.

Usage example

import gym
import myosuite
import deprl

# we can also change the reset_type of the environment here
env = gym.make('myoLegWalk-v0', reset_type='random')
policy = deprl.load_baseline(env)

for ep in range(5):
    obs = env.reset()
    for i in range(1000):
        action = policy(obs)
        next_obs, reward, done, info = env.step(action)
        env.sim.renderer.render_to_window()
        obs = next_obs
        if done:
            break

For the other baselines, just use: env = gym.make(‘myoChallengeRelocateP1-v0’) or env = gym.make(‘myoChallengeChaseTagP1-v0’)

You can also use noisy policy steps with:

import gym
import myosuite
import deprl

# we can also change the reset_type of the environment here
env = gym.make('myoLegWalk-v0', reset_type='random')
policy = deprl.load_baseline(env)

for ep in range(5):
    obs = env.reset()
    for i in range(1000):
        # we use a noisy policy here
        action = policy.noisy_test_step(obs)
        next_obs, reward, done, info = env.step(action)
        env.sim.renderer.render_to_window()
        obs = next_obs
        if done:
            break

This can affect your performance positively or negatively, depending on the task!